Statistical methods in model discrimination
Statistical methods in model discrimination
复制标题
模型判别中的统计方法
DOI：
10.1002/cjce.5450480213
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发表时间：
1970
影响因子：
2.1
通讯作者：
P. Reilly
中科院分区：
文献类型：
--
作者：
P. Reilly
特别是在化学动力学领域，以及化学和工程领域的其他领域，最近人们越来越关注使用统计数据来区分竞争对手的模型。有两个相关的基本问题。第一个是设计实验，这些实验对于确定几种可能的数学模型中哪一个是“正确的”模型而言将提供最丰富的信息。简单地说明了用不同的方法解决这个问题，例如 Roth、Box 和 Hill 等人的准则，包括 R. S. Hawkins 和作者在预期熵变方面所做的一些工作。第二个问题是收到数据后进行分析。通过示例描述了解决该问题的可能性和贝叶斯方法的样本。
Particularly in the chemical kinetic field but also in others in chemistry and engineering there has been much recent attention on the use of statistics in discriminating between rival models. There are two related basic problems. The first is to design experiments which will be most informative in determining which of several possible mathematical models is the “correct” one. Simple illustrations are given of the solution of this problem by different methods, such as the criteria of Roth, Box and Hill and of others, including some work done by R. S. Hawkins and the author on expected entropy change. The second problem is to analyze the data when received. A sampling of likelihood and Bayesian approaches to this problem is described with examples.